AIMC Topic: Image Processing, Computer-Assisted

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The beating heart: artificial intelligence for cardiovascular application in the clinic.

Magma (New York, N.Y.)
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantl...

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

Toxicologic pathology
We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary...

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Journal of the science of food and agriculture
BACKGROUND: Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial fo...

When an extra rejection class meets out-of-distribution detection in long-tailed image classification.

Neural networks : the official journal of the International Neural Network Society
Detecting Out-of-Distribution (OOD) inputs is essential for reliable deep learning in the open world. However, most existing OOD detection methods have been developed based on training sets that exhibit balanced class distributions, making them susce...

Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation genera...

Cross-Modal Graph Contrastive Learning with Cellular Images.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, chemistry, and medicine. State-of-the-art methods employ graph neural networks and self-supervised learning (SSL) to learn unlabe...

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation.

Computers in biology and medicine
This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provid...

Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

Archives of microbiology
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating una...

Research on multi-defects classification detection method for solar cells based on deep learning.

PloS one
Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detect...

VascuConNet: an enhanced connectivity network for vascular segmentation.

Medical & biological engineering & computing
Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge ...